Semantic Multimodal Compression for Wearable sensing Systems

Venue

Publication Year

Authors

BibTeX

Abstract

Wearable sensing systems (WSS's) are emerging as an important class of distributed
embedded systems in application domains ranging from medical to military. Such
systems can be expensive and power hungry due to their multi sensor implementations
that require constant use, yet by nature they demand low-cost and low-power
implementations. Semantic multimodal compression (SMC) mitigates these metrics in
terms of data size by leveraging the natural tendency of signals in many types of
embedded sensing systems to be composed of phases. In our driving example of a
medical shoe with an insole lined with pressure sensors, we find that the natural
airborne, landing, and take-off segments have sharply different and repetitive
properties. SMC models and compresses each segment independently, selecting the
best compression scheme for each segment and thus reducing total transmission
energy.